Recent approaches to machine composition use neural networks (NNs),
hoping to approximate how humans compose.
Chu et al [1]} generate a melody with a hierarchical NN
that encodes a composition strategy for pop music, and then accompany the
melody with chords and percussion. However, this music lacks hierarchical
temporal structure.
Boulanger-Lewandowski et al [2]} investigate hierarchical
temporal dependencies and long-term polyphonic structure.
Inspired by how an opening theme often recurs at a song's end,
they detect patterns with a recurrent temporal restricted Boltzmann machine (RTRBM).
This can represent more complicated temporal distributions of notes.
Similarly, Huang and Wu [3]} generate structured music
with a 2-layer Long Short Term Memory (LSTM) network.
Although the resulting music often sounds plausible,
it cannot produce clearly repeated melodic themes, just like a Markov
resynthesis of the text of the famous poem “Jabberwocky” is unlikely
to replicate the identical opening and closing stanzas of the original.
Despite the LSTM network's theoretical capability of long-term memory, it
fails to generalize to arbitrary time lengths [4]},
and its generated melodies remain unimaginative.
